Evidence-based practice is defined as the integration of best research evidence with clinical expertise and consumer preferences. However, little attention has been devoted to how to integrate consumer preferences into evidence-based practice in the treatment of major depressive disorder. No practical clinical methodology is available that provides real-time, consumer-weighting of preferences that would permit empirical findings to be used to individualize treatment choice for each mental health consumer with major depressive disorder. The overall goal of this R34 application is to develop and pilot a multi-attribute decision modeling approach in which clinical treatment decisions for people seeking treatment for major depressive disorder in a community mental health setting are guided by evidence-based practice data that has been customized to the preferences of individual consumers. We will apply multi-attribute decision modeling to match up consumers'ratings of their preferences regarding specific treatment attributes (i.e., efficacy, safety, tolerability) to the performance of available treatments as measured by meta-analytic data on each of the attributes (e.g., response rate;incidence of adverse events). Three development steps are proposed here: (1) compile information from existing meta-analyses, or conduct meta-analyses as needed, on the performance of existing evidence-based pharmacotherapies and psychotherapies for major depressive disorder in regard to a list of salient treatment attributes (efficacy;adverse events;tolerability;time commitment), (2) conduct a survey of 80 consumers and 40 clinicians from a community mental health center to evaluate the importance of various specified treatment attributes, and solicit additional treatment attributes deemed to be important, in the treatment of major depressive disorder, and (3) conduct a study examining the feasibility, ease of use, and predictive validity of 3 measures for assessing consumer preferences in regard to a final list of treatment attributes. This final study will be conducted using 72 consumers seeking treatment for major depressive disorder in a community mental health center, with preferences being used to predict duration of time each consumer stays on the treatment recommended to them at the agency. Results of these studies will be used (in future work) to develop a software product that provides real-time assessment of consumer preferences together with a matching of the preferences to attributes of evidence-based treatments for major depressive disorder so that an individualized treatment recommendation is produced to guide the clinician in decision making. Public Health Relevance: Major depressive disorder is one of the most common psychiatric disorders and is associated with considerable social and occupational disability. Incorporating consumer preferences into treatment will facilitate the tailoring of evidence-based practice to the individual and potentially increase consumer satisfaction and improve outcomes.